adirik/flux-cinestill
/
flux-cinestill
Select a model
View profile
adirik/flux-cinestill
Flux lora, use "CNSTLL" to trigger
adirik/stylemc-old
Text-Guided Image Generation and Manipulation
adirik/lightweight-openpose
PyTorch version of Lightweight OpenPose as introduced in "Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose"
adirik/t2i-adapter-sdxl-lineart
Modify images using line art
adirik/t2i-adapter-sdxl-canny
Modify images using canny edges
adirik/t2i-adapter-sdxl-sketch
Modify images using sketches
adirik/t2i-adapter-sdxl-openpose
Modify images using human pose
adirik/t2i-adapter-sdxl-depth-midas
Modify images using depth maps
adirik/inst-inpaint
Inst-Inpaint: Instructing to Remove Objects with Diffusion Models
adirik/owlvit-base-patch32
Zero-shot / open vocabulary object detection
adirik/deforum-kandinsky-2-2
Generate videos from text prompts with Kandinsky-2.2
adirik/grounding-dino
Detect everything with language!
adirik/wonder3d
Generates 3D assets from images
adirik/mvdream
Generate 3D assets using text descriptions
adirik/codet
Detects objects in an image
adirik/titanet-large
Performs speaker identity verification
adirik/styletts2
Generates speech from text
adirik/texture
Generate texture for your mesh with text prompts
adirik/kosmos-g
Kosmos-G: Generating Images in Context with Multimodal Large Language Models
adirik/masactrl-stable-diffusion-v1-4
Edit real or generated images
adirik/masactrl-anything-v4-0
Edit real or generated images
Prediction
adirik/flux-cinestill:0a6ad486c5589dd78647fb44f0527bcae57f112e49b722455b289baa897232c5Input
- model
- dev
- prompt
- CNSTLL, Road trip, view through car window of desert highway
- lora_scale
- 0.6
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "CNSTLL, Road trip, view through car window of desert highway\n", "lora_scale": 0.6, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run adirik/flux-cinestill using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "adirik/flux-cinestill:0a6ad486c5589dd78647fb44f0527bcae57f112e49b722455b289baa897232c5", { input: { model: "dev", prompt: "CNSTLL, Road trip, view through car window of desert highway\n", lora_scale: 0.6, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, extra_lora_scale: 0.8, num_inference_steps: 28 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun adirik/flux-cinestill using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "adirik/flux-cinestill:0a6ad486c5589dd78647fb44f0527bcae57f112e49b722455b289baa897232c5", input={ "model": "dev", "prompt": "CNSTLL, Road trip, view through car window of desert highway\n", "lora_scale": 0.6, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run adirik/flux-cinestill using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "adirik/flux-cinestill:0a6ad486c5589dd78647fb44f0527bcae57f112e49b722455b289baa897232c5", "input": { "model": "dev", "prompt": "CNSTLL, Road trip, view through car window of desert highway\\n", "lora_scale": 0.6, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-24T12:44:57.435576Z", "created_at": "2024-08-24T12:44:30.465000Z", "data_removed": false, "error": null, "id": "zjanp6dhg5rm00chghnb9bqj1m", "input": { "model": "dev", "prompt": "CNSTLL, Road trip, view through car window of desert highway\n", "lora_scale": 0.6, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 57711\nPrompt: CNSTLL, Road trip, view through car window of desert highway\ntxt2img mode\nUsing dev model\nfree=9659731640320\nDownloading weights\n2024-08-24T12:44:30Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpw4usuaxz/weights url=https://replicate.delivery/yhqm/GLO5igfTLkwNb6cZ7GvWXQ6e7BMvPZWOyJdgyWfDhHprqwrmA/trained_model.tar\n2024-08-24T12:44:33Z | INFO | [ Complete ] dest=/tmp/tmpw4usuaxz/weights size=\"688 MB\" total_elapsed=3.324s url=https://replicate.delivery/yhqm/GLO5igfTLkwNb6cZ7GvWXQ6e7BMvPZWOyJdgyWfDhHprqwrmA/trained_model.tar\nDownloaded weights in 3.37s\nLoaded LoRAs in 18.57s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.54it/s]\n 7%|▋ | 2/28 [00:00<00:06, 3.97it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.76it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.67it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.62it/s]\n 21%|██▏ | 6/28 [00:01<00:06, 3.59it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.58it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.56it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.55it/s]\n 36%|███▌ | 10/28 [00:02<00:05, 3.54it/s]\n 39%|███▉ | 11/28 [00:03<00:04, 3.52it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.55it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.54it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.54it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.54it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.54it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.54it/s]\n 64%|██████▍ | 18/28 [00:05<00:02, 3.54it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.54it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.53it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.54it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.54it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.54it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.53it/s]\n 89%|████████▉ | 25/28 [00:07<00:00, 3.54it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.54it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.54it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.53it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.56it/s]", "metrics": { "predict_time": 26.959945308, "total_time": 26.970576 }, "output": [ "https://replicate.delivery/yhqm/WGqB1k5eQuQTVSfffr8RfI4qs6PcNyZesqN5MKXnegR0kP8qJA/out-0.webp" ], "started_at": "2024-08-24T12:44:30.475631Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/zjanp6dhg5rm00chghnb9bqj1m", "cancel": "https://api.replicate.com/v1/predictions/zjanp6dhg5rm00chghnb9bqj1m/cancel" }, "version": "0a6ad486c5589dd78647fb44f0527bcae57f112e49b722455b289baa897232c5" }Generated inUsing seed: 57711 Prompt: CNSTLL, Road trip, view through car window of desert highway txt2img mode Using dev model free=9659731640320 Downloading weights 2024-08-24T12:44:30Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpw4usuaxz/weights url=https://replicate.delivery/yhqm/GLO5igfTLkwNb6cZ7GvWXQ6e7BMvPZWOyJdgyWfDhHprqwrmA/trained_model.tar 2024-08-24T12:44:33Z | INFO | [ Complete ] dest=/tmp/tmpw4usuaxz/weights size="688 MB" total_elapsed=3.324s url=https://replicate.delivery/yhqm/GLO5igfTLkwNb6cZ7GvWXQ6e7BMvPZWOyJdgyWfDhHprqwrmA/trained_model.tar Downloaded weights in 3.37s Loaded LoRAs in 18.57s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.54it/s] 7%|▋ | 2/28 [00:00<00:06, 3.97it/s] 11%|█ | 3/28 [00:00<00:06, 3.76it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.67it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.62it/s] 21%|██▏ | 6/28 [00:01<00:06, 3.59it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.58it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.56it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.55it/s] 36%|███▌ | 10/28 [00:02<00:05, 3.54it/s] 39%|███▉ | 11/28 [00:03<00:04, 3.52it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.55it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.54it/s] 50%|█████ | 14/28 [00:03<00:03, 3.54it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.54it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.54it/s] 61%|██████ | 17/28 [00:04<00:03, 3.54it/s] 64%|██████▍ | 18/28 [00:05<00:02, 3.54it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.54it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.53it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.54it/s] 79%|███████▊ | 22/28 [00:06<00:01, 3.54it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.54it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.53it/s] 89%|████████▉ | 25/28 [00:07<00:00, 3.54it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.54it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.54it/s] 100%|██████████| 28/28 [00:07<00:00, 3.53it/s] 100%|██████████| 28/28 [00:07<00:00, 3.56it/s]Prediction
adirik/flux-cinestill:216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938fIDebhsqv3dh1rm40chgk8bx8s66cStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- in the style of CNSTLL, an urban landscape with street sellers and a fish market at night, photorealistic
- lora_scale
- 0.6
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "in the style of CNSTLL, an urban landscape with street sellers and a fish market at night, photorealistic", "lora_scale": 0.6, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run adirik/flux-cinestill using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "adirik/flux-cinestill:216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938f", { input: { model: "dev", prompt: "in the style of CNSTLL, an urban landscape with street sellers and a fish market at night, photorealistic", lora_scale: 0.6, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, extra_lora_scale: 0.8, num_inference_steps: 28 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun adirik/flux-cinestill using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "adirik/flux-cinestill:216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938f", input={ "model": "dev", "prompt": "in the style of CNSTLL, an urban landscape with street sellers and a fish market at night, photorealistic", "lora_scale": 0.6, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run adirik/flux-cinestill using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "adirik/flux-cinestill:216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938f", "input": { "model": "dev", "prompt": "in the style of CNSTLL, an urban landscape with street sellers and a fish market at night, photorealistic", "lora_scale": 0.6, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-24T14:35:56.151735Z", "created_at": "2024-08-24T14:35:37.736000Z", "data_removed": false, "error": null, "id": "ebhsqv3dh1rm40chgk8bx8s66c", "input": { "model": "dev", "prompt": "in the style of CNSTLL, an urban landscape with street sellers and a fish market at night, photorealistic", "lora_scale": 0.6, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 33915\nPrompt: in the style of CNSTLL, an urban landscape with street sellers and a fish market at night, photorealistic\ntxt2img mode\nUsing dev model\nfree=9260846817280\nDownloading weights\n2024-08-24T14:35:37Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpbtq2xt5j/weights url=https://replicate.delivery/yhqm/3vhWyl3iACrCN9x53rEu7Lwit5hzZ9qDrKVi0wngSrNNheqJA/trained_model.tar\n2024-08-24T14:35:39Z | INFO | [ Complete ] dest=/tmp/tmpbtq2xt5j/weights size=\"172 MB\" total_elapsed=1.549s url=https://replicate.delivery/yhqm/3vhWyl3iACrCN9x53rEu7Lwit5hzZ9qDrKVi0wngSrNNheqJA/trained_model.tar\nDownloaded weights in 1.58s\nLoaded LoRAs in 10.00s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.53it/s]\n 7%|▋ | 2/28 [00:00<00:06, 3.97it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.77it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.68it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.62it/s]\n 21%|██▏ | 6/28 [00:01<00:06, 3.60it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.59it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.57it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.56it/s]\n 36%|███▌ | 10/28 [00:02<00:05, 3.56it/s]\n 39%|███▉ | 11/28 [00:03<00:04, 3.56it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.55it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.55it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.54it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.55it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.54it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.54it/s]\n 64%|██████▍ | 18/28 [00:05<00:02, 3.54it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.55it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.54it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.53it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.54it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.54it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.53it/s]\n 89%|████████▉ | 25/28 [00:07<00:00, 3.54it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.54it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.55it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.54it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.57it/s]", "metrics": { "predict_time": 18.408150834, "total_time": 18.415735 }, "output": [ "https://replicate.delivery/yhqm/KAdm9lK2TNYAGR2zTQKHDrzqbG51UvD2Nz0tWATMyeglD9qJA/out-0.webp" ], "started_at": "2024-08-24T14:35:37.743584Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ebhsqv3dh1rm40chgk8bx8s66c", "cancel": "https://api.replicate.com/v1/predictions/ebhsqv3dh1rm40chgk8bx8s66c/cancel" }, "version": "216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938f" }Generated inUsing seed: 33915 Prompt: in the style of CNSTLL, an urban landscape with street sellers and a fish market at night, photorealistic txt2img mode Using dev model free=9260846817280 Downloading weights 2024-08-24T14:35:37Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpbtq2xt5j/weights url=https://replicate.delivery/yhqm/3vhWyl3iACrCN9x53rEu7Lwit5hzZ9qDrKVi0wngSrNNheqJA/trained_model.tar 2024-08-24T14:35:39Z | INFO | [ Complete ] dest=/tmp/tmpbtq2xt5j/weights size="172 MB" total_elapsed=1.549s url=https://replicate.delivery/yhqm/3vhWyl3iACrCN9x53rEu7Lwit5hzZ9qDrKVi0wngSrNNheqJA/trained_model.tar Downloaded weights in 1.58s Loaded LoRAs in 10.00s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.53it/s] 7%|▋ | 2/28 [00:00<00:06, 3.97it/s] 11%|█ | 3/28 [00:00<00:06, 3.77it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.68it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.62it/s] 21%|██▏ | 6/28 [00:01<00:06, 3.60it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.59it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.57it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.56it/s] 36%|███▌ | 10/28 [00:02<00:05, 3.56it/s] 39%|███▉ | 11/28 [00:03<00:04, 3.56it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.55it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.55it/s] 50%|█████ | 14/28 [00:03<00:03, 3.54it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.55it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.54it/s] 61%|██████ | 17/28 [00:04<00:03, 3.54it/s] 64%|██████▍ | 18/28 [00:05<00:02, 3.54it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.55it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.54it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.53it/s] 79%|███████▊ | 22/28 [00:06<00:01, 3.54it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.54it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.53it/s] 89%|████████▉ | 25/28 [00:07<00:00, 3.54it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.54it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.55it/s] 100%|██████████| 28/28 [00:07<00:00, 3.54it/s] 100%|██████████| 28/28 [00:07<00:00, 3.57it/s]Prediction
adirik/flux-cinestill:216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938fIDk8babgk525rm20chgk99k2cwy8StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- in the style of CNSTLL, a woman hiking at dusk, photorealistic, cinestill 800T
- lora_scale
- 0.6
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "in the style of CNSTLL, a woman hiking at dusk, photorealistic, cinestill 800T", "lora_scale": 0.6, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run adirik/flux-cinestill using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "adirik/flux-cinestill:216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938f", { input: { model: "dev", prompt: "in the style of CNSTLL, a woman hiking at dusk, photorealistic, cinestill 800T", lora_scale: 0.6, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, extra_lora_scale: 0.8, num_inference_steps: 28 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun adirik/flux-cinestill using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "adirik/flux-cinestill:216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938f", input={ "model": "dev", "prompt": "in the style of CNSTLL, a woman hiking at dusk, photorealistic, cinestill 800T", "lora_scale": 0.6, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run adirik/flux-cinestill using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "adirik/flux-cinestill:216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938f", "input": { "model": "dev", "prompt": "in the style of CNSTLL, a woman hiking at dusk, photorealistic, cinestill 800T", "lora_scale": 0.6, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-24T14:38:05.249513Z", "created_at": "2024-08-24T14:37:46.641000Z", "data_removed": false, "error": null, "id": "k8babgk525rm20chgk99k2cwy8", "input": { "model": "dev", "prompt": "in the style of CNSTLL, a woman hiking at dusk, photorealistic, cinestill 800T", "lora_scale": 0.6, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 23954\nPrompt: in the style of CNSTLL, a woman hiking at dusk, photorealistic, cinestill 800T\ntxt2img mode\nUsing dev model\nfree=9767470870528\nDownloading weights\n2024-08-24T14:37:46Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpbrncmfdz/weights url=https://replicate.delivery/yhqm/3vhWyl3iACrCN9x53rEu7Lwit5hzZ9qDrKVi0wngSrNNheqJA/trained_model.tar\n2024-08-24T14:37:48Z | INFO | [ Complete ] dest=/tmp/tmpbrncmfdz/weights size=\"172 MB\" total_elapsed=1.616s url=https://replicate.delivery/yhqm/3vhWyl3iACrCN9x53rEu7Lwit5hzZ9qDrKVi0wngSrNNheqJA/trained_model.tar\nDownloaded weights in 1.65s\nLoaded LoRAs in 10.21s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.52it/s]\n 7%|▋ | 2/28 [00:00<00:06, 3.96it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.75it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.66it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.62it/s]\n 21%|██▏ | 6/28 [00:01<00:06, 3.59it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.57it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.56it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.55it/s]\n 36%|███▌ | 10/28 [00:02<00:05, 3.55it/s]\n 39%|███▉ | 11/28 [00:03<00:04, 3.54it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.54it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.53it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.54it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.54it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.53it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.52it/s]\n 64%|██████▍ | 18/28 [00:05<00:02, 3.52it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.53it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.52it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.51it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.53it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.53it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.52it/s]\n 89%|████████▉ | 25/28 [00:07<00:00, 3.53it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.53it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.51it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.52it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.55it/s]", "metrics": { "predict_time": 18.598344056, "total_time": 18.608513 }, "output": [ "https://replicate.delivery/yhqm/HV9IE99vOlK3ERSVZ8l8NZxlMZbVzAzez2MnXMjRn6xmE9qJA/out-0.webp" ], "started_at": "2024-08-24T14:37:46.651169Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/k8babgk525rm20chgk99k2cwy8", "cancel": "https://api.replicate.com/v1/predictions/k8babgk525rm20chgk99k2cwy8/cancel" }, "version": "216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938f" }Generated inUsing seed: 23954 Prompt: in the style of CNSTLL, a woman hiking at dusk, photorealistic, cinestill 800T txt2img mode Using dev model free=9767470870528 Downloading weights 2024-08-24T14:37:46Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpbrncmfdz/weights url=https://replicate.delivery/yhqm/3vhWyl3iACrCN9x53rEu7Lwit5hzZ9qDrKVi0wngSrNNheqJA/trained_model.tar 2024-08-24T14:37:48Z | INFO | [ Complete ] dest=/tmp/tmpbrncmfdz/weights size="172 MB" total_elapsed=1.616s url=https://replicate.delivery/yhqm/3vhWyl3iACrCN9x53rEu7Lwit5hzZ9qDrKVi0wngSrNNheqJA/trained_model.tar Downloaded weights in 1.65s Loaded LoRAs in 10.21s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.52it/s] 7%|▋ | 2/28 [00:00<00:06, 3.96it/s] 11%|█ | 3/28 [00:00<00:06, 3.75it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.66it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.62it/s] 21%|██▏ | 6/28 [00:01<00:06, 3.59it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.57it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.56it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.55it/s] 36%|███▌ | 10/28 [00:02<00:05, 3.55it/s] 39%|███▉ | 11/28 [00:03<00:04, 3.54it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.54it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.53it/s] 50%|█████ | 14/28 [00:03<00:03, 3.54it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.54it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.53it/s] 61%|██████ | 17/28 [00:04<00:03, 3.52it/s] 64%|██████▍ | 18/28 [00:05<00:02, 3.52it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.53it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.52it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.51it/s] 79%|███████▊ | 22/28 [00:06<00:01, 3.53it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.53it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.52it/s] 89%|████████▉ | 25/28 [00:07<00:00, 3.53it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.53it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.51it/s] 100%|██████████| 28/28 [00:07<00:00, 3.52it/s] 100%|██████████| 28/28 [00:07<00:00, 3.55it/s]Prediction
adirik/flux-cinestill:216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938fIDrathzs9kfxrm00chgk9rnj321wStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- in the style of CNSTLL, a white car parked in front of a gas station, night time, cinestill 800T
- lora_scale
- 0.6
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "in the style of CNSTLL, a white car parked in front of a gas station, night time, cinestill 800T", "lora_scale": 0.6, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run adirik/flux-cinestill using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "adirik/flux-cinestill:216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938f", { input: { model: "dev", prompt: "in the style of CNSTLL, a white car parked in front of a gas station, night time, cinestill 800T", lora_scale: 0.6, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, extra_lora_scale: 0.8, num_inference_steps: 28 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun adirik/flux-cinestill using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "adirik/flux-cinestill:216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938f", input={ "model": "dev", "prompt": "in the style of CNSTLL, a white car parked in front of a gas station, night time, cinestill 800T", "lora_scale": 0.6, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run adirik/flux-cinestill using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "adirik/flux-cinestill:216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938f", "input": { "model": "dev", "prompt": "in the style of CNSTLL, a white car parked in front of a gas station, night time, cinestill 800T", "lora_scale": 0.6, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-24T14:38:47.964812Z", "created_at": "2024-08-24T14:38:39.487000Z", "data_removed": false, "error": null, "id": "rathzs9kfxrm00chgk9rnj321w", "input": { "model": "dev", "prompt": "in the style of CNSTLL, a white car parked in front of a gas station, night time, cinestill 800T", "lora_scale": 0.6, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 51759\nPrompt: in the style of CNSTLL, a white car parked in front of a gas station, night time, cinestill 800T\ntxt2img mode\nUsing dev model\nWeights already loaded\nLoaded LoRAs in 0.04s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.52it/s]\n 7%|▋ | 2/28 [00:00<00:06, 3.94it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.74it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.66it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.61it/s]\n 21%|██▏ | 6/28 [00:01<00:06, 3.58it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.56it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.55it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.54it/s]\n 36%|███▌ | 10/28 [00:02<00:05, 3.54it/s]\n 39%|███▉ | 11/28 [00:03<00:04, 3.54it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.54it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.53it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.51it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.53it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.53it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.53it/s]\n 64%|██████▍ | 18/28 [00:05<00:02, 3.53it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.52it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.52it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.53it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.52it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.53it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.52it/s]\n 89%|████████▉ | 25/28 [00:07<00:00, 3.51it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.51it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.52it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.52it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.55it/s]", "metrics": { "predict_time": 8.467421274, "total_time": 8.477812 }, "output": [ "https://replicate.delivery/yhqm/v3ZpxG5gyA50JR1iiWanP1avoGFMOVf0fYGXOeU6DfKfORvaC/out-0.webp" ], "started_at": "2024-08-24T14:38:39.497391Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/rathzs9kfxrm00chgk9rnj321w", "cancel": "https://api.replicate.com/v1/predictions/rathzs9kfxrm00chgk9rnj321w/cancel" }, "version": "216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938f" }Generated inUsing seed: 51759 Prompt: in the style of CNSTLL, a white car parked in front of a gas station, night time, cinestill 800T txt2img mode Using dev model Weights already loaded Loaded LoRAs in 0.04s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.52it/s] 7%|▋ | 2/28 [00:00<00:06, 3.94it/s] 11%|█ | 3/28 [00:00<00:06, 3.74it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.66it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.61it/s] 21%|██▏ | 6/28 [00:01<00:06, 3.58it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.56it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.55it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.54it/s] 36%|███▌ | 10/28 [00:02<00:05, 3.54it/s] 39%|███▉ | 11/28 [00:03<00:04, 3.54it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.54it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.53it/s] 50%|█████ | 14/28 [00:03<00:03, 3.51it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.53it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.53it/s] 61%|██████ | 17/28 [00:04<00:03, 3.53it/s] 64%|██████▍ | 18/28 [00:05<00:02, 3.53it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.52it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.52it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.53it/s] 79%|███████▊ | 22/28 [00:06<00:01, 3.52it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.53it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.52it/s] 89%|████████▉ | 25/28 [00:07<00:00, 3.51it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.51it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.52it/s] 100%|██████████| 28/28 [00:07<00:00, 3.52it/s] 100%|██████████| 28/28 [00:07<00:00, 3.55it/s]Prediction
adirik/flux-cinestill:216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938fID0pdag5vzthrm00chgkbs9a8c3cStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- in the style of CNSTLL, a group of people talking and drinking beer at the pub in front a window, cinestill 800T, photorealistic
- lora_scale
- 0.6
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "in the style of CNSTLL, a group of people talking and drinking beer at the pub in front a window, cinestill 800T, photorealistic", "lora_scale": 0.6, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run adirik/flux-cinestill using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "adirik/flux-cinestill:216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938f", { input: { model: "dev", prompt: "in the style of CNSTLL, a group of people talking and drinking beer at the pub in front a window, cinestill 800T, photorealistic", lora_scale: 0.6, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, extra_lora_scale: 0.8, num_inference_steps: 28 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun adirik/flux-cinestill using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "adirik/flux-cinestill:216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938f", input={ "model": "dev", "prompt": "in the style of CNSTLL, a group of people talking and drinking beer at the pub in front a window, cinestill 800T, photorealistic", "lora_scale": 0.6, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run adirik/flux-cinestill using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "adirik/flux-cinestill:216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938f", "input": { "model": "dev", "prompt": "in the style of CNSTLL, a group of people talking and drinking beer at the pub in front a window, cinestill 800T, photorealistic", "lora_scale": 0.6, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-24T14:43:43.227045Z", "created_at": "2024-08-24T14:43:21.172000Z", "data_removed": false, "error": null, "id": "0pdag5vzthrm00chgkbs9a8c3c", "input": { "model": "dev", "prompt": "in the style of CNSTLL, a group of people talking and drinking beer at the pub in front a window, cinestill 800T, photorealistic", "lora_scale": 0.6, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 50680\nPrompt: in the style of CNSTLL, a group of people talking and drinking beer at the pub in front a window, cinestill 800T, photorealistic\ntxt2img mode\nUsing dev model\nLoaded LoRAs in 10.11s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.54it/s]\n 7%|▋ | 2/28 [00:00<00:06, 3.91it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.72it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.63it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.59it/s]\n 21%|██▏ | 6/28 [00:01<00:06, 3.56it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.54it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.53it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.53it/s]\n 36%|███▌ | 10/28 [00:02<00:05, 3.51it/s]\n 39%|███▉ | 11/28 [00:03<00:04, 3.51it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.51it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.51it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.51it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.50it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.50it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.50it/s]\n 64%|██████▍ | 18/28 [00:05<00:02, 3.50it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.50it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.50it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.50it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.50it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.50it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.50it/s]\n 89%|████████▉ | 25/28 [00:07<00:00, 3.50it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.50it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.50it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.50it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.52it/s]", "metrics": { "predict_time": 18.577725171, "total_time": 22.055045 }, "output": [ "https://replicate.delivery/yhqm/PByDqy7naMJJAlcBjdaQRkVLBYEKCgXYzmiI3Re9sR3PH9qJA/out-0.webp" ], "started_at": "2024-08-24T14:43:24.649320Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/0pdag5vzthrm00chgkbs9a8c3c", "cancel": "https://api.replicate.com/v1/predictions/0pdag5vzthrm00chgkbs9a8c3c/cancel" }, "version": "216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938f" }Generated inUsing seed: 50680 Prompt: in the style of CNSTLL, a group of people talking and drinking beer at the pub in front a window, cinestill 800T, photorealistic txt2img mode Using dev model Loaded LoRAs in 10.11s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.54it/s] 7%|▋ | 2/28 [00:00<00:06, 3.91it/s] 11%|█ | 3/28 [00:00<00:06, 3.72it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.63it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.59it/s] 21%|██▏ | 6/28 [00:01<00:06, 3.56it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.54it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.53it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.53it/s] 36%|███▌ | 10/28 [00:02<00:05, 3.51it/s] 39%|███▉ | 11/28 [00:03<00:04, 3.51it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.51it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.51it/s] 50%|█████ | 14/28 [00:03<00:03, 3.51it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.50it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.50it/s] 61%|██████ | 17/28 [00:04<00:03, 3.50it/s] 64%|██████▍ | 18/28 [00:05<00:02, 3.50it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.50it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.50it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.50it/s] 79%|███████▊ | 22/28 [00:06<00:01, 3.50it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.50it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.50it/s] 89%|████████▉ | 25/28 [00:07<00:00, 3.50it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.50it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.50it/s] 100%|██████████| 28/28 [00:07<00:00, 3.50it/s] 100%|██████████| 28/28 [00:07<00:00, 3.52it/s]Prediction
adirik/flux-cinestill:216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938fIDy65k0davq9rm60chgkdrkbb9gcStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- in the style of CNSTLL , urban landscape, people fishing on Galata bridge in Istanbul at night
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "in the style of CNSTLL , urban landscape, people fishing on Galata bridge in Istanbul at night", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run adirik/flux-cinestill using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "adirik/flux-cinestill:216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938f", { input: { model: "dev", prompt: "in the style of CNSTLL , urban landscape, people fishing on Galata bridge in Istanbul at night", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, extra_lora_scale: 0.8, num_inference_steps: 28 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun adirik/flux-cinestill using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "adirik/flux-cinestill:216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938f", input={ "model": "dev", "prompt": "in the style of CNSTLL , urban landscape, people fishing on Galata bridge in Istanbul at night", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run adirik/flux-cinestill using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "adirik/flux-cinestill:216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938f", "input": { "model": "dev", "prompt": "in the style of CNSTLL , urban landscape, people fishing on Galata bridge in Istanbul at night", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-24T14:47:51.268900Z", "created_at": "2024-08-24T14:47:34.074000Z", "data_removed": false, "error": null, "id": "y65k0davq9rm60chgkdrkbb9gc", "input": { "model": "dev", "prompt": "in the style of CNSTLL , urban landscape, people fishing on Galata bridge in Istanbul at night", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 19502\nPrompt: in the style of CNSTLL , urban landscape, people fishing on Galata bridge in Istanbul at night\ntxt2img mode\nUsing dev model\nLoaded LoRAs in 9.07s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.66it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.21it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.95it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.84it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.77it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.73it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.72it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.70it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.69it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.68it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.68it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.68it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.67it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.67it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.68it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.68it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.67it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.67it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.68it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.68it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.67it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.67it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.68it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.68it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.68it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.67it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.68it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.68it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.70it/s]", "metrics": { "predict_time": 17.186433884, "total_time": 17.1949 }, "output": [ "https://replicate.delivery/yhqm/0okKZOOvyUZDGFz5VFufOYFjwCKIrNQvQ4LeSGyz4jrXS6VTA/out-0.webp" ], "started_at": "2024-08-24T14:47:34.082466Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/y65k0davq9rm60chgkdrkbb9gc", "cancel": "https://api.replicate.com/v1/predictions/y65k0davq9rm60chgkdrkbb9gc/cancel" }, "version": "216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938f" }Generated inUsing seed: 19502 Prompt: in the style of CNSTLL , urban landscape, people fishing on Galata bridge in Istanbul at night txt2img mode Using dev model Loaded LoRAs in 9.07s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.66it/s] 7%|▋ | 2/28 [00:00<00:06, 4.21it/s] 11%|█ | 3/28 [00:00<00:06, 3.95it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.84it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.77it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.73it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.72it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.70it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.69it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.68it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.68it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.68it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.67it/s] 50%|█████ | 14/28 [00:03<00:03, 3.67it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.68it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.68it/s] 61%|██████ | 17/28 [00:04<00:02, 3.67it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.67it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.68it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.68it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.67it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.67it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.68it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.68it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.68it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.67it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.68it/s] 100%|██████████| 28/28 [00:07<00:00, 3.68it/s] 100%|██████████| 28/28 [00:07<00:00, 3.70it/s]Prediction
adirik/flux-cinestill:216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938fIDz90tbfk47drm20chgkg9fxb11rStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- in the style of CNSTLL , photo of New York City at night, 4k
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 2.5
- output_quality
- 80
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "in the style of CNSTLL , photo of New York City at night, 4k", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 2.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run adirik/flux-cinestill using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "adirik/flux-cinestill:216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938f", { input: { model: "dev", prompt: "in the style of CNSTLL , photo of New York City at night, 4k", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 2.5, output_quality: 80, extra_lora_scale: 0.8, num_inference_steps: 28 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun adirik/flux-cinestill using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "adirik/flux-cinestill:216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938f", input={ "model": "dev", "prompt": "in the style of CNSTLL , photo of New York City at night, 4k", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 2.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run adirik/flux-cinestill using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "adirik/flux-cinestill:216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938f", "input": { "model": "dev", "prompt": "in the style of CNSTLL , photo of New York City at night, 4k", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 2.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-24T14:53:20.988723Z", "created_at": "2024-08-24T14:53:03.931000Z", "data_removed": false, "error": null, "id": "z90tbfk47drm20chgkg9fxb11r", "input": { "model": "dev", "prompt": "in the style of CNSTLL , photo of New York City at night, 4k", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 2.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 63529\nPrompt: in the style of CNSTLL , photo of New York City at night, 4k\ntxt2img mode\nUsing dev model\nLoaded LoRAs in 8.90s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.69it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.25it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.96it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.83it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.78it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.74it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.72it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.70it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.69it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.69it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.68it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.67it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.68it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.68it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.67it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.67it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.67it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.68it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.67it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.66it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.67it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.67it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.67it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.66it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.67it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.67it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.67it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.67it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.70it/s]", "metrics": { "predict_time": 17.048015349, "total_time": 17.057723 }, "output": [ "https://replicate.delivery/yhqm/bYobZuwZkOaZBBeMz0crqzyBdUliW53ia3s3MSpZJ6KwL9qJA/out-0.webp" ], "started_at": "2024-08-24T14:53:03.940708Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/z90tbfk47drm20chgkg9fxb11r", "cancel": "https://api.replicate.com/v1/predictions/z90tbfk47drm20chgkg9fxb11r/cancel" }, "version": "216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938f" }Generated inUsing seed: 63529 Prompt: in the style of CNSTLL , photo of New York City at night, 4k txt2img mode Using dev model Loaded LoRAs in 8.90s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.69it/s] 7%|▋ | 2/28 [00:00<00:06, 4.25it/s] 11%|█ | 3/28 [00:00<00:06, 3.96it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.83it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.78it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.74it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.72it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.70it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.69it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.69it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.68it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.67it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.68it/s] 50%|█████ | 14/28 [00:03<00:03, 3.68it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.67it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.67it/s] 61%|██████ | 17/28 [00:04<00:02, 3.67it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.68it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.67it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.66it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.67it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.67it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.67it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.66it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.67it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.67it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.67it/s] 100%|██████████| 28/28 [00:07<00:00, 3.67it/s] 100%|██████████| 28/28 [00:07<00:00, 3.70it/s]Prediction
adirik/flux-cinestill:216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938fID8bgmdxj5d1rm60chgm0rytva98StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- CNSTLL, portrait of a woman standing against a door, night time, high resolution, 4k, cinestill 800t
- lora_scale
- 0.6
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3
- output_quality
- 80
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "CNSTLL, portrait of a woman standing against a door, night time, high resolution, 4k, cinestill 800t\n", "lora_scale": 0.6, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run adirik/flux-cinestill using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "adirik/flux-cinestill:216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938f", { input: { model: "dev", prompt: "CNSTLL, portrait of a woman standing against a door, night time, high resolution, 4k, cinestill 800t\n", lora_scale: 0.6, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3, output_quality: 80, extra_lora_scale: 0.8, num_inference_steps: 28 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun adirik/flux-cinestill using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "adirik/flux-cinestill:216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938f", input={ "model": "dev", "prompt": "CNSTLL, portrait of a woman standing against a door, night time, high resolution, 4k, cinestill 800t\n", "lora_scale": 0.6, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run adirik/flux-cinestill using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "adirik/flux-cinestill:216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938f", "input": { "model": "dev", "prompt": "CNSTLL, portrait of a woman standing against a door, night time, high resolution, 4k, cinestill 800t\\n", "lora_scale": 0.6, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-24T15:29:18.125404Z", "created_at": "2024-08-24T15:28:58.728000Z", "data_removed": false, "error": null, "id": "8bgmdxj5d1rm60chgm0rytva98", "input": { "model": "dev", "prompt": "CNSTLL, portrait of a woman standing against a door, night time, high resolution, 4k, cinestill 800t\n", "lora_scale": 0.6, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 4506\nPrompt: CNSTLL, portrait of a woman standing against a door, night time, high resolution, 4k, cinestill 800t\ntxt2img mode\nUsing dev model\nfree=9839779209216\nDownloading weights\n2024-08-24T15:28:58Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpdr4ixfvh/weights url=https://replicate.delivery/yhqm/3vhWyl3iACrCN9x53rEu7Lwit5hzZ9qDrKVi0wngSrNNheqJA/trained_model.tar\n2024-08-24T15:29:00Z | INFO | [ Complete ] dest=/tmp/tmpdr4ixfvh/weights size=\"172 MB\" total_elapsed=2.118s url=https://replicate.delivery/yhqm/3vhWyl3iACrCN9x53rEu7Lwit5hzZ9qDrKVi0wngSrNNheqJA/trained_model.tar\nDownloaded weights in 2.15s\nLoaded LoRAs in 11.04s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.55it/s]\n 7%|▋ | 2/28 [00:00<00:06, 3.98it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.78it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.69it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.64it/s]\n 21%|██▏ | 6/28 [00:01<00:06, 3.61it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.59it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.58it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.57it/s]\n 36%|███▌ | 10/28 [00:02<00:05, 3.57it/s]\n 39%|███▉ | 11/28 [00:03<00:04, 3.56it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.56it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.56it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.56it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.56it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.55it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.55it/s]\n 64%|██████▍ | 18/28 [00:05<00:02, 3.55it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.55it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.55it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.55it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.55it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.55it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.55it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.55it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.55it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.56it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.55it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.58it/s]", "metrics": { "predict_time": 19.385723191, "total_time": 19.397404 }, "output": [ "https://replicate.delivery/yhqm/uzYBDUdl3qqGEZQRLr310BKloBcEeCapyiLleSCd7O5N56VTA/out-0.webp" ], "started_at": "2024-08-24T15:28:58.739681Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/8bgmdxj5d1rm60chgm0rytva98", "cancel": "https://api.replicate.com/v1/predictions/8bgmdxj5d1rm60chgm0rytva98/cancel" }, "version": "216a43b9975de9768114644bbf8cd0cba54a923c6d0f65adceaccfc9383a938f" }Generated inUsing seed: 4506 Prompt: CNSTLL, portrait of a woman standing against a door, night time, high resolution, 4k, cinestill 800t txt2img mode Using dev model free=9839779209216 Downloading weights 2024-08-24T15:28:58Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpdr4ixfvh/weights url=https://replicate.delivery/yhqm/3vhWyl3iACrCN9x53rEu7Lwit5hzZ9qDrKVi0wngSrNNheqJA/trained_model.tar 2024-08-24T15:29:00Z | INFO | [ Complete ] dest=/tmp/tmpdr4ixfvh/weights size="172 MB" total_elapsed=2.118s url=https://replicate.delivery/yhqm/3vhWyl3iACrCN9x53rEu7Lwit5hzZ9qDrKVi0wngSrNNheqJA/trained_model.tar Downloaded weights in 2.15s Loaded LoRAs in 11.04s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.55it/s] 7%|▋ | 2/28 [00:00<00:06, 3.98it/s] 11%|█ | 3/28 [00:00<00:06, 3.78it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.69it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.64it/s] 21%|██▏ | 6/28 [00:01<00:06, 3.61it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.59it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.58it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.57it/s] 36%|███▌ | 10/28 [00:02<00:05, 3.57it/s] 39%|███▉ | 11/28 [00:03<00:04, 3.56it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.56it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.56it/s] 50%|█████ | 14/28 [00:03<00:03, 3.56it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.56it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.55it/s] 61%|██████ | 17/28 [00:04<00:03, 3.55it/s] 64%|██████▍ | 18/28 [00:05<00:02, 3.55it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.55it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.55it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.55it/s] 79%|███████▊ | 22/28 [00:06<00:01, 3.55it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.55it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.55it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.55it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.55it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.56it/s] 100%|██████████| 28/28 [00:07<00:00, 3.55it/s] 100%|██████████| 28/28 [00:07<00:00, 3.58it/s]
Want to make some of these yourself?
Run this model